Neural network nuclear power plant diagnostic equipment

摘要

PURPOSE:To diagnose a nuclear power plant after learning without any knowledge base by utilizing a neural network which can be calculated from the coupling between the output of a neural element and outputs of other neural elements including itself. CONSTITUTION:The neural network consists of neural elements in N input layers 40a, L intermediate layers Z(40b), and M output layers 40c and the neural elements 40 are simulated by neural cells of a living body. The neural elements 40 are connected by coupling elements 41 and the respective outputs of the neural elements are multiplied by coupling weight and outputted to neural elements in next layers to simulate synapsis. Here, N is the sum of the number Na of alarm states obtained from the nuclear plant, the number Nb of pressure values, and the number Nc of temperature values, and M is the number of accident causes. The coupling weight is calculated by learning algorithm from learning data on measurement data, the alarm, pressure, temperature, etc., obtained by simulation, etc., the accident causes, etc. After the learning, the accident causes are estimated actually for the alarm and others to diagnose the abnormality of the nuclear plant without any knowledge base.